HeartScope: Predictive Analysis of Cardiovascular Health Data

The dataset consists of 303 observations and 14 variables in total. We have :

• 7 categorical variables: sex, cp(chest pain type), fbs(fasting blood sugar exceeds 120 mg/dl or not ), restecg(resting electrocardiographic results), exang(presence of exercise-induced angina), slope(the slope of the peak exercise ST segment), thal(thallium stress test).

• 7 numerical variables: age, trestbps(resting blood pressure), chol(serum cholesterol levels), thalach(maximum heart rate), oldpeak(distance between baseline of ST segment), ca(number of colored major blood vessels), num(diagnosis of heart disease)

The purpose of our analysis is to uncover key patterns and risk factors associated with coronary heart diseases. We aim to identify factors that highly correlates with heart disease and promote early disease intervention in patients.

We identified several key predictors of heart disease, including the type of chest pain (cp), number of detectable vessels (thal), major vessels colored by fluoroscopy (ca), and ST depression (oldpeak).

In clinical practice, the diagnosis of heart disease often involves individual differences and a multitude of complex factors. The correlated factors can provide important reference points for clinicians in the diagnostic process, helping them to identify potential risk factors for heart disease at an earlier stage, thereby improving patients’ survival chances and overall health.

In summary, our study highlights the potential value of data analysis in enhancing the diagnosis and treatment of heart disease.

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